Abstract

Multi-channel near-infrared spectroscopy (NIRS) is increasingly used in empirical studies monitoring human brain activity. In a recent study, an independent component analysis (ICA) technique using time-delayed decorrelation was applied to NIRS signals since those signals reflect cerebral blood flow changes caused by task-induced responses as well as various artifacts. The decorrelation technique is important in NIRS-based analyses and may facilitate accurate separation of independent signals generated by oxygenated/deoxygenated hemoglobin concentration changes. We introduce an algorithm using time-delayed correlations that enable estimation of independent components (ICs) in which the number of components is fewer than that of observed sources; the conventional approach using a larger number of components may deteriorate settling of the solution. In a simulation, the algorithm was shown capable of estimating the number of ICs of virtually observed signals set by an experimenter, with the simulation reproducing seven sources where each was a mixture of three ICs and white noises. In addition, the algorithm was introduced in an experiment using ICs of NIRS signals observed during finger-tapping movements. Experimental results showed consistency and reproducibility of the estimated ICs that are attributed to patterns in the spatial distribution and temporal structure.